GE Global Research, the technology development arm of the General Electric Company, today announced research that could significantly impact the design of future wind turbine blades. Utilizing the power of high-performance computing (HPC) to perform complex calculations, GE engineers have overcome previous design constraints, allowing them to begin exploring ways to design reengineered wind blades that are low-noise and more prolific power-producers.
A limiting factor in developing more powerful wind turbines is noise. (Although one study showed that the noise from wind turbines is harmless, it can certainly be irritating.) One approach to boost the power output is to spin the rotors faster, but this also pushes the noise levels of the turbine beyond allowable limits. However, notes Giridhar Jothiprasad, a mechanical engineer with GE Global Research, “If you change the blade design to be quieter, you can spin the rotor faster to produce more power and still meet noise regulation standards.”
Partnering with the Sandia National Laboratories (Sandia) in Albuquerque, New Mexico, GE’s work focused on advancing wind turbine noise prediction methods. Aerodynamic blade noise is the dominant noise source on modern, utility-scale wind turbines and represents a key constraint in wind turbine design. In fact, GE predicts a 1 decibel quieter rotor design would result in a two-percent increase in annual energy yield per turbine. With approximately 240GW of new wind installations forecasted globally over the next five years, a two-percent increase would create 5GW of additional wind power capacity. That’s enough to power every household in New York City, Boston, and Los Angeles, combined.
“There’s no question, aerodynamic noise is a key constraint in wind turbine blade design today”, said Mark Jonkhof, Wind Technology Platform Leader at GE Global Research. “By using high-performance computing to advance current engineering models that are used to predict blade noise, we can build quieter rotors with greater blade tip velocity that produce more power. This not only means lower energy costs for consumers, but also a significant reduction in greenhouse gas emissions.”
Jonkhof added, “Having access to Sandia’s supercomputer was invaluable in our ability to conduct these experiments and make discoveries that will bolster wind power’s potential. Access and availability to HPC resources offers a critical advantage to companies trying to compete in a global environment.”
To ensure that GE’s wind blades do not pose noise issues today, airfoil level acoustic measurements are performed in wind tunnels, field measurements are done to validate acceptable noise levels, and noise-reducing operating modes are implemented in the control system. Better modeling will help maintain the current low wind turbine noise levels while boosting output.
“Sandia and other DOE national laboratories are using high-performance computing resources to tackle complex design problems in wind energy, such as reducing turbine blade noise to achieve significant reductions in cost-of-energy. Sandia helped GE gain valuable insights into blade noise mechanisms and how design engineers can improve their models,” said Matt Barone, of Sandia’s Aerosciences Department, formerly of the Wind Energy Technologies Department.
GE’s testing involved Sandia’s Red Mesa supercomputer running a high-fidelity Large Eddy Simulation (LES) code, developed at Stanford University, to predict the detailed fluid dynamic phenomena and resulting wind blade noise. For a period of three months, this LES simulation of the turbulent air flow past a wind blade section was continuously performed on the Red Mesa HPC. The resulting flow-field predictions yielded valuable insights that were used to assess current engineering design models, the assumptions they make that most impact noise predictions, and the accuracy and reliability of model choices.
“We found that high fidelity models can play a key role in accurately predicting trailing edge noise,” Jonkhof went on to say. “We believe that the results achieved from our simulations would, at the very least, lay the groundwork for improved noise design models